scholarly journals Research on Tire Marking Point Completeness Evaluation Based on K-Means Clustering Image Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4687 ◽  
Author(s):  
Yuan Yu ◽  
Jinsheng Ren ◽  
Qi Zhang ◽  
Weimin Yang ◽  
Zhiwei Jiao
Keyword(s):  
Image Segmentation ◽  
Machine Vision ◽  
High Precision ◽  
Clustering Algorithm ◽  
Dynamic Balance ◽  
Experimental Results ◽  
Quality Inspection ◽  
Point Evaluations ◽  
Smart Factories

The tire marking points of dynamic balance and uniformity play a crucial guiding role in tire installation. Incomplete marking points block the recognition of tire marking points, and then affect the installation of tires. It is usually necessary to evaluate the marking point completeness during the quality inspection of finished tires. In order to meet the high-precision requirements of the evaluation of tire marking point completeness in the smart factories, the K-means clustering algorithm is introduced to segment the image of marking points in this paper. The pixels within the contour of the marking point are weighted to calculate the marking point completeness on the basis of the image segmentation. The completeness is rated and evaluated by completeness calculation. The experimental results show that the accuracy of the marking point completeness ratings is 95%, and the accuracy of the marking point evaluations is 99%. The proposed method has an important guiding significance of practice to evaluate the tire marking point completeness during the tire quality inspection based on machine vision.

scholarly journals A High Precision Quality Inspection System for Steel Bars Based on Machine Vision

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2732 ◽  
Author(s):  
Xinman Zhang ◽  
Jiayu Zhang ◽  
Mei Ma ◽  
Zhiqi Chen ◽  
Shuangling Yue ◽  
...  
Keyword(s):  
Machine Vision ◽  
High Precision ◽  
Absolute Error ◽  
Detection Methods ◽  
Inspection System ◽  
Quality Inspection ◽  
Construction Site ◽  
High Definition ◽  
Steel Bar ◽  
Steel Bars

Steel bars play an important role in modern construction projects and their quality enormously affects the safety of buildings. It is urgent to detect whether steel bars meet the specifications or not. However, the existing manual detection methods are costly, slow and offer poor precision. In order to solve these problems, a high precision quality inspection system for steel bars based on machine vision is developed. We propose two algorithms: the sub-pixel boundary location method (SPBLM) and fast stitch method (FSM). A total of five sensors, including a CMOS, a level sensor, a proximity switch, a voltage sensor, and a current sensor have been used to detect the device conditions and capture image or video. The device could capture abundant and high-definition images and video taken by a uniform and stable smartphone at the construction site. Then data could be processed in real-time on a smartphone. Furthermore, the detection results, including steel bar diameter, spacing, and quantity would be given by a practical APP. The system has a rather high accuracy (as low as 0.04 mm (absolute error) and 0.002% (relative error) of calculating diameter and spacing; zero error in counting numbers of steel bars) when doing inspection tasks, and three parameters can be detected at the same time. None of these features are available in existing systems and the device and method can be widely used to steel bar quality inspection at the construction site.

An Efficient and Robust Kernelized Possibilistic C-Means Clustering Algorithm

2014 ◽  
Vol 962-965 ◽  
pp. 2881-2885
Author(s):  
Ya Ting Hu ◽  
Fu Heng Qu ◽  
Yao Hong Xue ◽  
Yong Yang
Keyword(s):  
Data Structure ◽  
High Precision ◽  
Data Reduction ◽  
Computational Efficiency ◽  
Clustering Algorithm ◽  
Original Data ◽  
Reduction Technique ◽  
Experimental Results ◽  
Weighted Clustering ◽  
Data Reduction Technique

To avoid the initialization sensitivity and low computational efficiency problems of the kernelized possibilistic c-means clustering algorithm (KPCM), a new clustering algorithm called efficient and robust kernelized possibilistic c-means clustering algorithm (ERKPCM) was proposed in this paper. ERKPCM improved KPCM by two ways. First, the data are refined by the data reduction technique, which makes it keep the data structure of the original data and have higher efficiency. Secondly, weighted clustering algorithm is executed several times to estimate cluster centers accurately, which makes it more robust to initializations and get more reasonable partitions. As a by-product, ERKPCM overcomes the problem of generating coincident clusters of KPCM. The contrast experimental results with conventional algorithms show that ERKPCM is more robust to initializations, and has a relatively high precision and efficiency.

IGNORANCE-BASED FUZZY CLUSTERING ALGORITHM

2010 ◽  
Vol 09 (03) ◽  
pp. 225-239
Author(s):  
ARANZAZU JURIO ◽  
MIGUEL PAGOLA ◽  
HUMBERTO BUSTINCE
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Data Classification ◽  
Experimental Results ◽  
Fuzzy Clustering Algorithm ◽  
Data Point

In this work an ignorance-based fuzzy clustering algorithm is presented. The algorithm is based on the entropy-based clustering algorithm proposed by Yao et al.[14]In our proposal, we calculate the total ignorance instead of using the entropy at each data point to select the cluster centers. The experimental results show that the ignorance-based clustering improves the data classification made by the EFC in image segmentation.

scholarly journals Image Segmentation Based on Intuitionistic Type-2 FCM Algorithm

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Zhongqiang Pan ◽  
Xiangjian Chen
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Experimental Results ◽  
Fcm Algorithm ◽  
Hybrid Clustering ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Clustering Algorithm ◽  
Segmentation Accuracy

Due to using the fuzzy clustering algorithm, the accuracy of image segmentation is nothigh enough. So one hybrid clustering algorithm combined with intuitionistic fuzzy factor and localspatial information is proposed. Experimental results show that the proposed algorithm is superiorto other methods in image segmentation accuracy and improves the robustness of the algorithm.

scholarly journals How the Multiplicity of Suggested Information Affects the Behavior of a User in a Recommender System

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 741
Author(s):  
Yuseok Ban ◽  
Kyungjae Lee
Keyword(s):  
High Precision ◽  
Recommender System ◽  
Visual Information ◽  
Experimental Results ◽  
Supplementary Information ◽  
Digital Platform ◽  
Different Types ◽  
Types Of Information

Many researchers have suggested improving the retention of a user in the digital platform using a recommender system. Recent studies show that there are many potential ways to assist users to find interesting items, other than high-precision rating predictions. In this paper, we study how the diverse types of information suggested to a user can influence their behavior. The types have been divided into visual information, evaluative information, categorial information, and narrational information. Based on our experimental results, we analyze how different types of supplementary information affect the performance of a recommender in terms of encouraging users to click more items or spend more time in the digital platform.

scholarly journals Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

2021 ◽  
Vol 11 (15) ◽  
pp. 6721
Author(s):  
Jinyeong Wang ◽  
Sanghwan Lee
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Image Data ◽  
Manufacturing Productivity ◽  
Smart Factories ◽  
Grayscale Images

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

A population-based automatic clustering algorithm for image segmentation

2021 ◽  
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Gerald Schaefer ◽  
Mahshid Helali Moghadam ◽  
Mehrdad Saadatmand ◽  
Mahdi Pedram
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Population Based ◽  
Automatic Clustering

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

A Novel Locality Sensitive K-Means Clustering Algorithm based on Core Clusters

2013 ◽  
Vol 321-324 ◽  
pp. 1939-1942
Author(s):  
Lei Gu
Keyword(s):  
Clustering Algorithm ◽  
Experimental Results ◽  
Clustering Method ◽  
Neighborhood Graph ◽  
The Core ◽  
Random Samples

The locality sensitive k-means clustering method has been presented recently. Although this approach can improve the clustering accuracies, it often gains the unstable clustering results because some random samples are employed for the initial centers. In this paper, an initialization method based on the core clusters is used for the locality sensitive k-means clustering. The core clusters can be formed by constructing the σ-neighborhood graph and their centers are regarded as the initial centers of the locality sensitive k-means clustering. To investigate the effectiveness of our approach, several experiments are done on three datasets. Experimental results show that our proposed method can improve the clustering performance compared to the previous locality sensitive k-means clustering.

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